Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Gas Chromatography: Types of Detectors-II01:19

Gas Chromatography: Types of Detectors-II

346
In gas chromatography, different detectors are employed to meet specific analytical needs. These detectors are often categorized based on their detection mechanisms and the types of compounds they are best suited to analyze. Thermal Conductivity Detectors (TCD), Flame Ionization Detectors (FID), and Electron Capture Detectors (ECD) represent common categories, each with unique operating principles and applications. However, beyond these, several other detectors are designed for more specialized...
346
Gas Chromatography: Overview of Detectors01:13

Gas Chromatography: Overview of Detectors

459
Detectors in gas chromatography (GC) help identify and quantify the components of a mixture by translating chemical properties into measurable signals, which are displayed on a chromatogram. Detectors can be categorized into two main types: destructive and non-destructive.
A non-destructive detector allows a sample to be analyzed without altering or consuming it, meaning the sample can be collected after detection for further analysis. Examples include thermal conductivity detectors and...
459
Gas Chromatography: Types of Detectors-I01:21

Gas Chromatography: Types of Detectors-I

389
There are different types of detectors used in gas chromatography, each with its own specific properties that make it suitable for detecting certain types of analytes. The most commonly used detectors in GC are thermal conductivity detector (TCD), flame ionization detector (FID), and electron capture detector (ECD).
TCD is the earliest and most widely used detector that operates by measuring the changes in the thermal conductivity of the carrier gas. When a sample compound enters the detector,...
389
Gas Chromatography: Introduction01:13

Gas Chromatography: Introduction

1.6K
Gas chromatography (GC) is a technique for separating and analyzing volatile compounds in a sample. Its primary purpose is to identify and quantify components in complex mixtures, making it essential in fields such as environmental analysis, pharmaceuticals, and petrochemicals. GC is also called vapor-phase chromatography (VPC) or gas-liquid partition chromatography (GLPC).
In GC,  a sample is vaporized and mixed with an inert carrier gas (the mobile phase), which transports it through a...
1.6K
High-Performance Liquid Chromatography: Types of Detectors01:15

High-Performance Liquid Chromatography: Types of Detectors

511
The role of the detectors in High-Performance Liquid Chromatography (HPLC) is to analyze the solutes as they exit from the chromatographic column. The detector recognizes the solute's property and generates corresponding electrical signals, which are converted into a readable graph of the detector's response versus elution time called a chromatogram at the computer. There are several types of HPLC detectors, each with its own advantages and limitations, depending on the analyte...
511
Gas Chromatography–Mass Spectrometry (GC–MS)01:14

Gas Chromatography–Mass Spectrometry (GC–MS)

4.1K
Gas chromatography–mass spectrometry (GC–MS) is the combination of analytical techniques of gas chromatography and mass spectrometry in a single instrument for analyzing a mixture of compounds. The gas chromatograph separates the compounds in the mixture, and the mass spectrometer analyzes each compound separately to determine the molecular masses and molecular structures.
A gas chromatograph consists of a long, narrow capillary column with a polysiloxane coating on the inner wall....
4.1K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Concise total synthesis of coerulescine by Cu(I)-catalyzed tandem reaction.

Journal of Asian natural products research·2026
Same author

Composite decoupling of temperature and strain in FBG sensors using physics-informed neural network (PINN).

Optics express·2026
Same author

Large Language Models for Ophthalmology Training in China: A Prospective Evaluation.

Ophthalmology science·2026
Same author

IRG1/Itaconate Inhibits Microglial Senescence-Like Transition by Modulating Mitochondrial Dynamics through Rhoa Alkylation in Subarachnoid Hemorrhage.

Aging and disease·2026
Same author

IGFBP1: A Key Regulatory Gene in the Oncogenesis and Progression of Esophageal Cancer.

Genes·2026
Same author

Growth Differentiation Factor 11 Is a Circulating Regulator of Oligodendrocyte Differentiation and CNS Myelin Formation and Repair.

CNS neuroscience & therapeutics·2026
Same journal

A Comprehensive Survey on Multimodal Recommender Systems: Taxonomy, Evaluation, and Future Directions.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Learning Topology-Aware Representations via Test-Time Adaptation for Anomaly Segmentation.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

TraGraph-GS: Trajectory Graph-based Gaussian Splatting for Arbitrary Large-Scale Scene Rendering.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

SWIFT: A Small-World Interaction Framework for Flow-Aware Trajectory Prediction in Autonomous Driving.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

HardFlow: Hard-Constrained Sampling for Flow-Matching Models Via Trajectory Optimization.

IEEE transactions on pattern analysis and machine intelligence·2026
查看所有相关文章

相关实验视频

Updated: Jun 15, 2025

Quantitative Detection of Trace Explosive Vapors by Programmed Temperature Desorption Gas Chromatography-Electron Capture Detector
07:57

Quantitative Detection of Trace Explosive Vapors by Programmed Temperature Desorption Gas Chromatography-Electron Capture Detector

Published on: July 25, 2014

19.9K

气体物体检测仪 气体物体检测仪

Kailai Zhou, Yibo Wang, Tao Lv

    IEEE transactions on pattern analysis and machine intelligence
    |August 26, 2024
    PubMed
    概括
    此摘要是机器生成的。

    本研究介绍了气体物体检测 (GOD),扩展计算机视觉以检测气体. 一个新的数据集和Voxel转移场 (VSF) 方法为这个具有挑战性的任务提供了基线.

    更多相关视频

    Infrared Degenerate Four-wave Mixing with Upconversion Detection for Quantitative Gas Sensing
    10:42

    Infrared Degenerate Four-wave Mixing with Upconversion Detection for Quantitative Gas Sensing

    Published on: March 22, 2019

    6.2K
    Detection of 3-Nitrotyrosine in Atmospheric Environments via a High-performance Liquid Chromatography-electrochemical Detector System
    07:32

    Detection of 3-Nitrotyrosine in Atmospheric Environments via a High-performance Liquid Chromatography-electrochemical Detector System

    Published on: January 30, 2019

    7.4K

    相关实验视频

    Last Updated: Jun 15, 2025

    Quantitative Detection of Trace Explosive Vapors by Programmed Temperature Desorption Gas Chromatography-Electron Capture Detector
    07:57

    Quantitative Detection of Trace Explosive Vapors by Programmed Temperature Desorption Gas Chromatography-Electron Capture Detector

    Published on: July 25, 2014

    19.9K
    Infrared Degenerate Four-wave Mixing with Upconversion Detection for Quantitative Gas Sensing
    10:42

    Infrared Degenerate Four-wave Mixing with Upconversion Detection for Quantitative Gas Sensing

    Published on: March 22, 2019

    6.2K
    Detection of 3-Nitrotyrosine in Atmospheric Environments via a High-performance Liquid Chromatography-electrochemical Detector System
    07:32

    Detection of 3-Nitrotyrosine in Atmospheric Environments via a High-performance Liquid Chromatography-electrochemical Detector System

    Published on: January 30, 2019

    7.4K

    科学领域:

    • 计算机视觉 计算机视觉
    • 深度学习 (Deep Learning) 是一种深度学习.
    • 科学成像科学成像技术

    背景情况:

    • 传统的物体检测优于刚性,固体的物体.
    • 气态物质带来了独特的检测挑战:低突出度,未定义的形状和模糊的边界.
    • 现有的深度学习对象检测方法不适合气体介质.

    研究的目的:

    • 介绍和探索气体物体检测 (GOD) 的新任务.
    • 开发一个基本的数据集和基准来评估气体物体检测算法.
    • 提出一种以物理为灵感的方法,用于模拟物体检测中的气体动力学.

    主要方法:

    • 构建GOD-Video数据集,包含各种气体的600个视频 (141,017).
    • 建立一个全面的基准框架级和视频级探测器评估.
    • 开发了以物理学为灵感的Voxel转移场 (VSF),以建模气体的几何不规则.

    主要成果:

    • VSF RCNN,将VSF整合到Faster RCNN中,为GOD建立了一个强大的基线.
    • GOD-Video数据集使气体物质检测方法的严格评估成为可能.
    • 证明了适应深度学习对象检测用于气体介质的可行性.

    结论:

    • 气体物体检测是一个可行的,尽管具有挑战性的,计算机视觉的新前沿.
    • 拟议的VSF方法提供了一个有希望的方法来处理气体物体检测的复杂性.
    • 鼓励进一步的研究,以推进在这个未经探索的领域的技术.